Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

Aashish C. Gupta, Guillaume Cazoulat, Mais Al Taie, Sireesha Yedururi, Bastien Rigaud, Austin Castelo, John Wood, Cenji Yu, Caleb O’Connor, Usama Salem, Jessica Albuquerque Marques Silva, Aaron Kyle Jones, Molly McCulloch, Bruno C. Odisio, Eugene J. Koay, Kristy K. Brock

Research output: Contribution to journalArticlepeer-review


Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net (MpaU-Net) and 3d full resolution of nnU-Net (MnnU-Net) to determine the best architecture (BA). BA was used with vessels (MVess) and spleen (Mseg+spleen) to assess the impact on segment contouring. Models were trained, validated, and tested on 160 (CRTTrain), 40 (CRTVal), 33 (CLS), 25 (CCH) and 20 (CPVE) CECT of LC patients. MnnU-Net outperformed MpaU-Net across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03–0.05 (p < 0.05). Mseg+spleen, and MnnU-Net were not statistically different (p > 0.05), however, both were slightly better than MVess by DSC up to 0.02. The final model, Mseg+spleen, showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5–8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score ≥ 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.

Original languageEnglish (US)
Article number4678
Pages (from-to)4678
JournalScientific Reports
Issue number1
StatePublished - Feb 26 2024


  • Humans
  • Spleen/diagnostic imaging
  • Deep Learning
  • Tomography, X-Ray Computed/methods
  • Liver Neoplasms/diagnostic imaging
  • Image Processing, Computer-Assisted/methods

ASJC Scopus subject areas

  • General


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